One of the main drivers of satellite imagery analytics is the rapid and accurate detection of objects of interest over broad areas. In recent years, convolutional neural networks (CNNs) have been applied to object detection on multimedia imagery, achieving remarkable success on benchmark data sets. The main idea behind the best performing architectures is to derive region proposals, i.e., bounding boxes for the objects present in the image, and then infer the object class for each bounding box.

Boat detection on satellite imagery is important for a number of reasons, including identification of illegal fishing activities, wide-area surveillance of exclusive economic zones and maritime traffic monitoring. Automatic Identification System (AIS) transmissions are mandatory for vessels of given size and type; reliable boat detection can serve as a complement to AIS data and can help authorities detect suspicious or illegal activity by identifying silent boats.

Floods can have a catastrophicimpact on local communities and their economies, resulting in loss of human life and property, damage in agriculture and destruction of infrastructure. Accurate and near real-time derived flood maps are of paramount importance in directing search and rescue operations and relief efforts.
We have developed a practical and scalable solution to flood mapping in the form of an unsupervised flood water detector which utilizes 4- or 8-band DigitalGlobe imagery and can process an entire strip in a matter of minutes on a default GBDX instance.

Picture being able to select an arbitrary region of the world in your browser and within it rapidly locate all the objects of interest. The advancement of deep learning combined with easy access to high resolution satellite imagery enabled by platforms such as GBDX are making accurate object detection at scale an attainable goal. However, the application of deep learning on satellite imagery is still in its infancy and many questions are open with regards to its efficacy at a global scale.

Estimating oil reserves from high-resolution satellite imagery has becomeratherpopular in our budding geospatial-analytics-from-space industry. Oil is typically stored in tanks with floating roofs, therefore the fill of the oil tank can be estimated from the shadow cast on the inside of the tank as the lid sinks. A pretty neat idea.

There are large regions of the planet which, although inhabited, remain unmapped to this day.
In the past, DigitalGlobe has launched crowdsourcing campaigns to detect remote population centers
in Ethiopia, Sudan
and Swaziland in support of NGO vaccination and aid distribution initiatives.
Beyond DigitalGlobe, there are otherinitiatives under way to
fill in the gaps in the global map, aiding first responders in their effort to provide relief to vulnerable, yet inaccessible, people.

Tasks are the bread and butter of the GBDX Platform.
This walkthrough will take you through the steps of creating a task that can
run your Python code on GBDX. We will start with a very simple task and then cover the more advanced
case of creating a machine learning task. We will also demonstrate how you can
setup your machine learning task to run on a GPU.

Information about built environments is extremely valuable
to insurance companies, tax assessors, and public agencies, as it empowers a wide range of decision-making including
urban and regional planning and management, risk estimation, and emergency response.
Extracting this information using human analysts to scour satellite imagery
is prohibitively expensive and time consuming. Feature extraction and machine
learning algorithms are the only viable way to perform this type of attribution
at scale.